Overview

Dataset statistics

Number of variables 45
Number of observations 158
Missing cells 596
Missing cells (%) 8.4%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 55.7 KiB
Average record size in memory 360.8 B

Variable types

Numeric 15
Categorical 30

Dataset

Description A dataset about students' socio-demographic characteristics
Creator Matteo Busso, Massimo Stefan
Author Fausto Giunchiglia, Ivano Bison, Matteo Busso, Ronald Chenu-Abente, Marcelo Rodas Britez, Can Gunel, Giuseppe Veltri, Amalia de Götzen, Peter Kun, Amarsanaa Ganbold, Altangerel Chagnaa, George Gaskell, Miriam Bidoglia, Luca Cernuzzi, Alethia Hume, Jose Luis Zarza, Daniele Miorandi, Carlo Caprini
URL
Copyright (c) KnowDive 2022

Variable descriptions

degree programme
c locfac
u pract

Alerts

userid is highly correlated with creationTs High correlation
creationTs is highly correlated with userid High correlation
c_musgall is highly correlated with c_perf_art High correlation
c_perf_art is highly correlated with c_musgall High correlation
c_watch_sp is highly correlated with c_team_sp High correlation
c_ind_sp is highly correlated with c_team_sp High correlation
c_team_sp is highly correlated with c_watch_sp and 1 other fields High correlation
c_accom is highly correlated with c_locfac High correlation
c_locfac is highly correlated with c_accom High correlation
u_active is highly correlated with u_read and 3 other fields High correlation
u_read is highly correlated with u_active and 4 other fields High correlation
u_essay is highly correlated with u_read and 2 other fields High correlation
u_org is highly correlated with u_active and 4 other fields High correlation
u_balance is highly correlated with u_active and 2 other fields High correlation
u_assess is highly correlated with u_active and 4 other fields High correlation
u_theory is highly correlated with u_read and 1 other fields High correlation
u_pract is highly correlated with u_balance High correlation
userid is highly correlated with creationTs High correlation
creationTs is highly correlated with userid High correlation
excitement is highly correlated with suprapersonal High correlation
suprapersonal is highly correlated with excitement High correlation
c_musgall is highly correlated with c_perf_art High correlation
c_perf_art is highly correlated with c_musgall High correlation
c_watch_sp is highly correlated with c_team_sp High correlation
c_ind_sp is highly correlated with c_team_sp High correlation
c_team_sp is highly correlated with c_watch_sp and 1 other fields High correlation
c_accom is highly correlated with c_locfac High correlation
c_locfac is highly correlated with c_accom High correlation
u_active is highly correlated with u_read and 3 other fields High correlation
u_read is highly correlated with u_active and 4 other fields High correlation
u_essay is highly correlated with u_read and 2 other fields High correlation
u_org is highly correlated with u_active and 4 other fields High correlation
u_balance is highly correlated with u_active and 2 other fields High correlation
u_assess is highly correlated with u_active and 4 other fields High correlation
u_theory is highly correlated with u_read and 1 other fields High correlation
u_pract is highly correlated with u_balance High correlation
userid is highly correlated with creationTs High correlation
creationTs is highly correlated with userid High correlation
c_watch_sp is highly correlated with c_team_sp High correlation
c_team_sp is highly correlated with c_watch_sp High correlation
c_accom is highly correlated with c_locfac High correlation
c_locfac is highly correlated with c_accom High correlation
u_read is highly correlated with u_essay and 2 other fields High correlation
u_essay is highly correlated with u_read and 2 other fields High correlation
u_org is highly correlated with u_essay and 2 other fields High correlation
u_balance is highly correlated with u_org High correlation
u_assess is highly correlated with u_read and 3 other fields High correlation
u_theory is highly correlated with u_read and 1 other fields High correlation
department is highly correlated with university and 3 other fields High correlation
university is highly correlated with department and 4 other fields High correlation
nationality is highly correlated with department and 4 other fields High correlation
locale is highly correlated with department and 3 other fields High correlation
degree_programme is highly correlated with university and 1 other fields High correlation
accommodation is highly correlated with nationality High correlation
appId is highly correlated with department and 4 other fields High correlation
userid is highly correlated with locale and 6 other fields High correlation
gender is highly correlated with lastUpdateTs and 1 other fields High correlation
locale is highly correlated with userid and 8 other fields High correlation
nationality is highly correlated with userid and 10 other fields High correlation
creationTs is highly correlated with userid and 5 other fields High correlation
lastUpdateTs is highly correlated with userid and 8 other fields High correlation
appId is highly correlated with userid and 7 other fields High correlation
dateOfBirth - year is highly correlated with locale and 6 other fields High correlation
department is highly correlated with userid and 24 other fields High correlation
degree_programme is highly correlated with locale and 7 other fields High correlation
university is highly correlated with userid and 7 other fields High correlation
accommodation is highly correlated with locale and 2 other fields High correlation
excitement is highly correlated with department and 3 other fields High correlation
promotion is highly correlated with u_org High correlation
existence is highly correlated with u_assess and 1 other fields High correlation
suprapersonal is highly correlated with department and 3 other fields High correlation
interactive is highly correlated with department and 3 other fields High correlation
normative is highly correlated with nationality and 5 other fields High correlation
extraversion is highly correlated with department and 2 other fields High correlation
agreeableness is highly correlated with extraversion and 2 other fields High correlation
conscientiousness is highly correlated with nationality and 3 other fields High correlation
neuroticism is highly correlated with nationality and 3 other fields High correlation
openness is highly correlated with department and 3 other fields High correlation
c_food is highly correlated with extraversion and 2 other fields High correlation
c_eating is highly correlated with department and 1 other fields High correlation
c_lit is highly correlated with dateOfBirth - year and 5 other fields High correlation
c_creatlit is highly correlated with creationTs and 2 other fields High correlation
c_perf_mus is highly correlated with c_perf_art High correlation
c_plays is highly correlated with dateOfBirth - year and 2 other fields High correlation
c_perf_plays is highly correlated with department and 2 other fields High correlation
c_musgall is highly correlated with c_lit and 3 other fields High correlation
c_perf_art is highly correlated with department and 4 other fields High correlation
c_watch_sp is highly correlated with normative and 1 other fields High correlation
c_ind_sp is highly correlated with c_team_sp High correlation
c_team_sp is highly correlated with neuroticism and 2 other fields High correlation
c_accom is highly correlated with c_locfac and 1 other fields High correlation
c_locfac is highly correlated with c_accom and 1 other fields High correlation
u_active is highly correlated with conscientiousness and 6 other fields High correlation
u_read is highly correlated with c_lit and 7 other fields High correlation
u_essay is highly correlated with c_creatlit and 5 other fields High correlation
u_org is highly correlated with promotion and 9 other fields High correlation
u_balance is highly correlated with department and 8 other fields High correlation
u_assess is highly correlated with nationality and 11 other fields High correlation
u_theory is highly correlated with department and 7 other fields High correlation
u_pract is highly correlated with existence and 7 other fields High correlation
locale has 19 (12.0%) missing values Missing
nationality has 52 (32.9%) missing values Missing
dateOfBirth - year has 5 (3.2%) missing values Missing
department has 21 (13.3%) missing values Missing
degree_programme has 17 (10.8%) missing values Missing
university has 16 (10.1%) missing values Missing
accommodation has 20 (12.7%) missing values Missing
excitement has 14 (8.9%) missing values Missing
promotion has 15 (9.5%) missing values Missing
existence has 14 (8.9%) missing values Missing
suprapersonal has 14 (8.9%) missing values Missing
interactive has 15 (9.5%) missing values Missing
normative has 14 (8.9%) missing values Missing
extraversion has 14 (8.9%) missing values Missing
agreeableness has 14 (8.9%) missing values Missing
conscientiousness has 14 (8.9%) missing values Missing
neuroticism has 14 (8.9%) missing values Missing
openness has 14 (8.9%) missing values Missing
c_food has 12 (7.6%) missing values Missing
c_eating has 12 (7.6%) missing values Missing
c_lit has 13 (8.2%) missing values Missing
c_creatlit has 13 (8.2%) missing values Missing
c_perf_mus has 14 (8.9%) missing values Missing
c_plays has 14 (8.9%) missing values Missing
c_perf_plays has 14 (8.9%) missing values Missing
c_musgall has 13 (8.2%) missing values Missing
c_perf_art has 13 (8.2%) missing values Missing
c_watch_sp has 14 (8.9%) missing values Missing
c_ind_sp has 13 (8.2%) missing values Missing
c_team_sp has 14 (8.9%) missing values Missing
c_accom has 13 (8.2%) missing values Missing
c_locfac has 13 (8.2%) missing values Missing
u_active has 13 (8.2%) missing values Missing
u_read has 13 (8.2%) missing values Missing
u_essay has 13 (8.2%) missing values Missing
u_org has 13 (8.2%) missing values Missing
u_balance has 14 (8.9%) missing values Missing
u_assess has 13 (8.2%) missing values Missing
u_theory has 13 (8.2%) missing values Missing
u_pract has 13 (8.2%) missing values Missing
userid has unique values Unique
creationTs has unique values Unique

Reproduction

Analysis started 2022-07-04 18:35:52.952516
Analysis finished 2022-07-04 18:37:07.401571
Duration 1 minute and 14.45 seconds
Software version pandas-profiling v3.2.0
Download configuration config.json

Variables

userid
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct 158
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 369.1455696
Minimum 5
Maximum 510
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:07.560842 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 5
5-th percentile 76.5
Q1 342.5
median 406.5
Q3 457.5
95-th percentile 501.3
Maximum 510
Range 505
Interquartile range (IQR) 115

Descriptive statistics

Standard deviation 128.9933996
Coefficient of variation (CV) 0.3494377564
Kurtosis 1.109699525
Mean 369.1455696
Median Absolute Deviation (MAD) 57.5
Skewness -1.402227683
Sum 58325
Variance 16639.29715
Monotonicity Not monotonic
2022-07-04T20:37:07.857982 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
477 1
0.6%
18 1
0.6%
445 1
0.6%
446 1
0.6%
341 1
0.6%
389 1
0.6%
336 1
0.6%
81 1
0.6%
349 1
0.6%
353 1
0.6%
Other values (148) 148
93.7%
Value Count Frequency (%)
5 1
0.6%
8 1
0.6%
10 1
0.6%
18 1
0.6%
22 1
0.6%
40 1
0.6%
49 1
0.6%
51 1
0.6%
81 1
0.6%
85 1
0.6%
Value Count Frequency (%)
510 1
0.6%
509 1
0.6%
508 1
0.6%
507 1
0.6%
506 1
0.6%
505 1
0.6%
504 1
0.6%
503 1
0.6%
501 1
0.6%
500 1
0.6%

gender
Categorical

HIGH CORRELATION

Distinct 4
Distinct (%) 2.5%
Missing 0
Missing (%) 0.0%
Memory size 1.4 KiB
M
68
F
66
not-say
23
non-binary
1

Length

Max length 10
Median length 1
Mean length 1.930379747
Min length 1

Characters and Unicode

Total characters 305
Distinct characters 12
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.6%

Sample

1st row M
2nd row M
3rd row F
4th row M
5th row M

Common Values

Value Count Frequency (%)
M 68
43.0%
F 66
41.8%
not-say 23
14.6%
non-binary 1
0.6%

Length

2022-07-04T20:37:08.113757 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:08.357156 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
m 68
43.0%
f 66
41.8%
not-say 23
14.6%
non-binary 1
0.6%

Most occurring characters

Value Count Frequency (%)
M 68
22.3%
F 66
21.6%
n 26
8.5%
o 24
7.9%
- 24
7.9%
a 24
7.9%
y 24
7.9%
t 23
7.5%
s 23
7.5%
b 1
0.3%
Other values (2) 2
0.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 147
48.2%
Uppercase Letter 134
43.9%
Dash Punctuation 24
7.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
n 26
17.7%
o 24
16.3%
a 24
16.3%
y 24
16.3%
t 23
15.6%
s 23
15.6%
b 1
0.7%
i 1
0.7%
r 1
0.7%
Uppercase Letter
Value Count Frequency (%)
M 68
50.7%
F 66
49.3%
Dash Punctuation
Value Count Frequency (%)
- 24
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 281
92.1%
Common 24
7.9%

Most frequent character per script

Latin
Value Count Frequency (%)
M 68
24.2%
F 66
23.5%
n 26
9.3%
o 24
8.5%
a 24
8.5%
y 24
8.5%
t 23
8.2%
s 23
8.2%
b 1
0.4%
i 1
0.4%
Common
Value Count Frequency (%)
- 24
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 305
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
M 68
22.3%
F 66
21.6%
n 26
8.5%
o 24
7.9%
- 24
7.9%
a 24
7.9%
y 24
7.9%
t 23
7.5%
s 23
7.5%
b 1
0.3%
Other values (2) 2
0.7%

locale
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 17
Distinct (%) 12.2%
Missing 19
Missing (%) 12.0%
Memory size 1.4 KiB
it_IT
29
mn
24
es_PY
20
en_GB
20
en_US
17
Other values (12)
29

Length

Max length 5
Median length 5
Mean length 4.179856115
Min length 2

Characters and Unicode

Total characters 581
Distinct characters 32
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 9 ?
Unique (%) 6.5%

Sample

1st row it_IT
2nd row it_IT
3rd row it_IT
4th row en_US
5th row it_IT

Common Values

Value Count Frequency (%)
it_IT 29
18.4%
mn 24
15.2%
es_PY 20
12.7%
en_GB 20
12.7%
en_US 17
10.8%
da 11
7.0%
en_IN 5
3.2%
zh_CN 4
2.5%
es_CL 1
0.6%
fr_BE 1
0.6%
Other values (7) 7
4.4%
(Missing) 19
12.0%

Length

2022-07-04T20:37:08.583375 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
it_it 29
20.9%
mn 24
17.3%
es_py 20
14.4%
en_gb 20
14.4%
en_us 17
12.2%
da 11
7.9%
en_in 5
3.6%
zh_cn 4
2.9%
hu 1
0.7%
ko 1
0.7%
Other values (7) 7
5.0%

Most occurring characters

Value Count Frequency (%)
_ 101
17.4%
n 67
11.5%
e 66
11.4%
I 34
5.9%
t 30
5.2%
T 30
5.2%
i 29
5.0%
m 24
4.1%
s 23
4.0%
P 21
3.6%
Other values (22) 156
26.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 278
47.8%
Uppercase Letter 202
34.8%
Connector Punctuation 101
17.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
n 67
24.1%
e 66
23.7%
t 30
10.8%
i 29
10.4%
m 24
8.6%
s 23
8.3%
a 11
4.0%
d 11
4.0%
h 5
1.8%
z 4
1.4%
Other values (7) 8
2.9%
Uppercase Letter
Value Count Frequency (%)
I 34
16.8%
T 30
14.9%
P 21
10.4%
B 21
10.4%
Y 20
9.9%
G 20
9.9%
S 18
8.9%
U 17
8.4%
N 9
4.5%
C 6
3.0%
Other values (4) 6
3.0%
Connector Punctuation
Value Count Frequency (%)
_ 101
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 480
82.6%
Common 101
17.4%

Most frequent character per script

Latin
Value Count Frequency (%)
n 67
14.0%
e 66
13.8%
I 34
7.1%
t 30
6.2%
T 30
6.2%
i 29
6.0%
m 24
5.0%
s 23
4.8%
P 21
4.4%
B 21
4.4%
Other values (21) 135
28.1%
Common
Value Count Frequency (%)
_ 101
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 581
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
_ 101
17.4%
n 67
11.5%
e 66
11.4%
I 34
5.9%
t 30
5.2%
T 30
5.2%
i 29
5.0%
m 24
4.1%
s 23
4.0%
P 21
3.6%
Other values (22) 156
26.9%

nationality
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 19
Distinct (%) 17.9%
Missing 52
Missing (%) 32.9%
Memory size 1.4 KiB
italy
24
mongolia
22
paraguai
20
denmark
12
england
4
Other values (14)
24

Length

Max length 9
Median length 8
Mean length 6.594339623
Min length 2

Characters and Unicode

Total characters 699
Distinct characters 22
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 8 ?
Unique (%) 7.5%

Sample

1st row italy
2nd row italy
3rd row italy
4th row italy
5th row italy

Common Values

Value Count Frequency (%)
italy 24
15.2%
mongolia 22
13.9%
paraguai 20
12.7%
denmark 12
7.6%
england 4
2.5%
spain 3
1.9%
india 3
1.9%
us 3
1.9%
cina 3
1.9%
poland 2
1.3%
Other values (9) 10
6.3%
(Missing) 52
32.9%

Length

2022-07-04T20:37:08.815152 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
italy 24
22.6%
mongolia 22
20.8%
paraguai 20
18.9%
denmark 12
11.3%
england 4
3.8%
spain 3
2.8%
india 3
2.8%
us 3
2.8%
cina 3
2.8%
france 2
1.9%
Other values (9) 10
9.4%

Most occurring characters

Value Count Frequency (%)
a 143
20.5%
i 83
11.9%
n 58
8.3%
l 56
8.0%
o 51
7.3%
g 49
7.0%
r 38
5.4%
m 37
5.3%
t 27
3.9%
u 27
3.9%
Other values (12) 130
18.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 699
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 143
20.5%
i 83
11.9%
n 58
8.3%
l 56
8.0%
o 51
7.3%
g 49
7.0%
r 38
5.4%
m 37
5.3%
t 27
3.9%
u 27
3.9%
Other values (12) 130
18.6%

Most occurring scripts

Value Count Frequency (%)
Latin 699
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
a 143
20.5%
i 83
11.9%
n 58
8.3%
l 56
8.0%
o 51
7.3%
g 49
7.0%
r 38
5.4%
m 37
5.3%
t 27
3.9%
u 27
3.9%
Other values (12) 130
18.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 699
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 143
20.5%
i 83
11.9%
n 58
8.3%
l 56
8.0%
o 51
7.3%
g 49
7.0%
r 38
5.4%
m 37
5.3%
t 27
3.9%
u 27
3.9%
Other values (12) 130
18.6%

creationTs
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct 158
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 1633971485
Minimum 1612534817
Maximum 1638885017
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:09.076735 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 1612534817
5-th percentile 1615498166
Q1 1637366011
median 1637581494
Q3 1637842400
95-th percentile 1638727576
Maximum 1638885017
Range 26350200
Interquartile range (IQR) 476389

Descriptive statistics

Standard deviation 8496617.27
Coefficient of variation (CV) 0.005199978917
Kurtosis 1.253521908
Mean 1633971485
Median Absolute Deviation (MAD) 238259.5
Skewness -1.782986109
Sum 2.581674946 × 10 11
Variance 7.219250504 × 10 13
Monotonicity Not monotonic
2022-07-04T20:37:09.380928 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1638288097 1
0.6%
1612984267 1
0.6%
1637676774 1
0.6%
1637679809 1
0.6%
1637347252 1
0.6%
1637575303 1
0.6%
1637327989 1
0.6%
1615504933 1
0.6%
1637455687 1
0.6%
1637530064 1
0.6%
Other values (148) 148
93.7%
Value Count Frequency (%)
1612534817 1
0.6%
1612748009 1
0.6%
1612773674 1
0.6%
1612984267 1
0.6%
1613558866 1
0.6%
1614880553 1
0.6%
1615459320 1
0.6%
1615459817 1
0.6%
1615504933 1
0.6%
1615528396 1
0.6%
Value Count Frequency (%)
1638885017 1
0.6%
1638850986 1
0.6%
1638821925 1
0.6%
1638785137 1
0.6%
1638777952 1
0.6%
1638748477 1
0.6%
1638739796 1
0.6%
1638735217 1
0.6%
1638726227 1
0.6%
1638641279 1
0.6%

lastUpdateTs
Real number (ℝ ≥0 )

HIGH CORRELATION

Distinct 151
Distinct (%) 95.6%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 1638260170
Minimum 1637154585
Maximum 1639040411
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:09.690945 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 1637154585
5-th percentile 1637581094
Q1 1638216759
median 1638240941
Q3 1638280296
95-th percentile 1638791468
Maximum 1639040411
Range 1885826
Interquartile range (IQR) 63537.5

Descriptive statistics

Standard deviation 303722.4959
Coefficient of variation (CV) 0.0001853933224
Kurtosis 3.372393798
Mean 1638260170
Median Absolute Deviation (MAD) 24246.5
Skewness -0.8055627841
Sum 2.588451069 × 10 11
Variance 9.224735449 × 10 10
Monotonicity Not monotonic
2022-07-04T20:37:10.003863 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1638257443 3
1.9%
1638216960 2
1.3%
1638216517 2
1.3%
1638257527 2
1.3%
1638216947 2
1.3%
1638257549 2
1.3%
1638216705 1
0.6%
1638216818 1
0.6%
1638217039 1
0.6%
1638240090 1
0.6%
Other values (141) 141
89.2%
Value Count Frequency (%)
1637154585 1
0.6%
1637248086 1
0.6%
1637324436 1
0.6%
1637339993 1
0.6%
1637403080 1
0.6%
1637421340 1
0.6%
1637498949 1
0.6%
1637521649 1
0.6%
1637591584 1
0.6%
1637605001 1
0.6%
Value Count Frequency (%)
1639040411 1
0.6%
1639004696 1
0.6%
1638885730 1
0.6%
1638885094 1
0.6%
1638878254 1
0.6%
1638851844 1
0.6%
1638831896 1
0.6%
1638822539 1
0.6%
1638785985 1
0.6%
1638785549 1
0.6%

appId
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct 5
Distinct (%) 3.2%
Missing 0
Missing (%) 0.0%
Memory size 1.4 KiB
sVq48ryxGe
46
GuS9StLxrv
33
2O4ppqNC6f
31
LUOmwNXfZq
26
HnH5iaO6VI
22

Length

Max length 10
Median length 10
Mean length 10
Min length 10

Characters and Unicode

Total characters 1580
Distinct characters 34
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row GuS9StLxrv
2nd row GuS9StLxrv
3rd row GuS9StLxrv
4th row GuS9StLxrv
5th row GuS9StLxrv

Common Values

Value Count Frequency (%)
sVq48ryxGe 46
29.1%
GuS9StLxrv 33
20.9%
2O4ppqNC6f 31
19.6%
LUOmwNXfZq 26
16.5%
HnH5iaO6VI 22
13.9%

Length

2022-07-04T20:37:10.291477 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:10.549560 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
svq48ryxge 46
29.1%
gus9stlxrv 33
20.9%
2o4ppqnc6f 31
19.6%
luomwnxfzq 26
16.5%
hnh5iao6vi 22
13.9%

Most occurring characters

Value Count Frequency (%)
q 103
6.5%
O 79
5.0%
r 79
5.0%
x 79
5.0%
G 79
5.0%
4 77
4.9%
V 68
4.3%
S 66
4.2%
p 62
3.9%
L 59
3.7%
Other values (24) 829
52.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 735
46.5%
Uppercase Letter 583
36.9%
Decimal Number 262
16.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
q 103
14.0%
r 79
10.7%
x 79
10.7%
p 62
8.4%
f 57
7.8%
s 46
6.3%
e 46
6.3%
y 46
6.3%
t 33
4.5%
u 33
4.5%
Other values (6) 151
20.5%
Uppercase Letter
Value Count Frequency (%)
O 79
13.6%
G 79
13.6%
V 68
11.7%
S 66
11.3%
L 59
10.1%
N 57
9.8%
H 44
7.5%
C 31
5.3%
X 26
4.5%
Z 26
4.5%
Other values (2) 48
8.2%
Decimal Number
Value Count Frequency (%)
4 77
29.4%
6 53
20.2%
8 46
17.6%
9 33
12.6%
2 31
11.8%
5 22
8.4%

Most occurring scripts

Value Count Frequency (%)
Latin 1318
83.4%
Common 262
16.6%

Most frequent character per script

Latin
Value Count Frequency (%)
q 103
7.8%
O 79
6.0%
r 79
6.0%
x 79
6.0%
G 79
6.0%
V 68
5.2%
S 66
5.0%
p 62
4.7%
L 59
4.5%
f 57
4.3%
Other values (18) 587
44.5%
Common
Value Count Frequency (%)
4 77
29.4%
6 53
20.2%
8 46
17.6%
9 33
12.6%
2 31
11.8%
5 22
8.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 1580
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
q 103
6.5%
O 79
5.0%
r 79
5.0%
x 79
5.0%
G 79
5.0%
4 77
4.9%
V 68
4.3%
S 66
4.2%
p 62
3.9%
L 59
3.7%
Other values (24) 829
52.5%

dateOfBirth - year
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Distinct 25
Distinct (%) 16.3%
Missing 5
Missing (%) 3.2%
Infinite 0
Infinite (%) 0.0%
Mean 1997.117647
Minimum 1971
Maximum 2009
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:10.814909 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 1971
5-th percentile 1984.6
Q1 1996
median 1999
Q3 2001
95-th percentile 2002
Maximum 2009
Range 38
Interquartile range (IQR) 5

Descriptive statistics

Standard deviation 5.711186134
Coefficient of variation (CV) 0.00285971442
Kurtosis 4.942668753
Mean 1997.117647
Median Absolute Deviation (MAD) 2
Skewness -1.977369454
Sum 305559
Variance 32.61764706
Monotonicity Not monotonic
2022-07-04T20:37:11.057389 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
Value Count Frequency (%)
1999 27
17.1%
2002 21
13.3%
1998 19
12.0%
2000 18
11.4%
2001 13
8.2%
1994 8
5.1%
1996 6
3.8%
1997 6
3.8%
1993 5
3.2%
1991 4
2.5%
Other values (15) 26
16.5%
(Missing) 5
3.2%
Value Count Frequency (%)
1971 1
0.6%
1974 1
0.6%
1978 1
0.6%
1979 1
0.6%
1983 1
0.6%
1984 3
1.9%
1985 1
0.6%
1986 2
1.3%
1988 1
0.6%
1989 2
1.3%
Value Count Frequency (%)
2009 1
0.6%
2003 4
2.5%
2002 21
13.3%
2001 13
8.2%
2000 18
11.4%
1999 27
17.1%
1998 19
12.0%
1997 6
3.8%
1996 6
3.8%
1995 3
1.9%

department
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 46
Distinct (%) 33.6%
Missing 21
Missing (%) 13.3%
Memory size 1.4 KiB
Faculty of science and technology
20
Information Engineering and Computer Science
19
School of Applied Sciences and Engineering
11
Business School
6
Medialogy (MED), BSc
5
Other values (41)
76

Length

Max length 75
Median length 50
Mean length 32.96350365
Min length 5

Characters and Unicode

Total characters 4516
Distinct characters 48
Distinct categories 7 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 21 ?
Unique (%) 15.3%

Sample

1st row Information Engineering and Computer Science
2nd row Information Engineering and Computer Science
3rd row Mathematics
4th row Information Engineering and Computer Science
5th row Industrial Engineering

Common Values

Value Count Frequency (%)
Faculty of science and technology 20
12.7%
Information Engineering and Computer Science 19
12.0%
School of Applied Sciences and Engineering 11
7.0%
Business School 6
3.8%
Medialogy (MED), BSc 5
3.2%
Department of Health Policy 5
3.2%
Law School 4
2.5%
European Institute 4
2.5%
Service Systems Design (SSD), MSc 3
1.9%
Innovative Communication Technologies And Entrepreneurship (ICTE), MSc 3
1.9%
Other values (36) 57
36.1%
(Missing) 21
13.3%

Length

2022-07-04T20:37:11.354076 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
and 66
10.9%
of 62
10.2%
science 45
7.4%
engineering 33
5.4%
school 29
4.8%
faculty 23
3.8%
information 22
3.6%
department 20
3.3%
msc 20
3.3%
technology 20
3.3%
Other values (84) 266
43.9%

Most occurring characters

Value Count Frequency (%)
469
10.4%
n 427
9.5%
e 417
9.2%
o 334
7.4%
i 288
6.4%
c 279
6.2%
t 223
4.9%
a 223
4.9%
l 161
3.6%
r 149
3.3%
Other values (38) 1546
34.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 3395
75.2%
Uppercase Letter 536
11.9%
Space Separator 469
10.4%
Other Punctuation 37
0.8%
Open Punctuation 33
0.7%
Close Punctuation 33
0.7%
Dash Punctuation 13
0.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
n 427
12.6%
e 417
12.3%
o 334
9.8%
i 288
8.5%
c 279
8.2%
t 223
6.6%
a 223
6.6%
l 161
4.7%
r 149
4.4%
s 125
3.7%
Other values (12) 769
22.7%
Uppercase Letter
Value Count Frequency (%)
S 147
27.4%
E 54
10.1%
I 49
9.1%
D 44
8.2%
M 43
8.0%
C 36
6.7%
A 28
5.2%
B 25
4.7%
F 23
4.3%
T 18
3.4%
Other values (10) 69
12.9%
Other Punctuation
Value Count Frequency (%)
, 36
97.3%
/ 1
2.7%
Space Separator
Value Count Frequency (%)
469
100.0%
Open Punctuation
Value Count Frequency (%)
( 33
100.0%
Close Punctuation
Value Count Frequency (%)
) 33
100.0%
Dash Punctuation
Value Count Frequency (%)
- 13
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 3931
87.0%
Common 585
13.0%

Most frequent character per script

Latin
Value Count Frequency (%)
n 427
10.9%
e 417
10.6%
o 334
8.5%
i 288
7.3%
c 279
7.1%
t 223
5.7%
a 223
5.7%
l 161
4.1%
r 149
3.8%
S 147
3.7%
Other values (32) 1283
32.6%
Common
Value Count Frequency (%)
469
80.2%
, 36
6.2%
( 33
5.6%
) 33
5.6%
- 13
2.2%
/ 1
0.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 4516
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
469
10.4%
n 427
9.5%
e 417
9.2%
o 334
7.4%
i 288
6.4%
c 279
6.2%
t 223
4.9%
a 223
4.9%
l 161
3.6%
r 149
3.3%
Other values (38) 1546
34.2%

degree_programme
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 13
Distinct (%) 9.2%
Missing 17
Missing (%) 10.8%
Memory size 1.4 KiB
MSc/MA
28
Undergraduate year 1
28
Undergraduate year 2
18
Undergraduate year 3
14
MSc/MA year 1
14
Other values (8)
39

Length

Max length 24
Median length 20
Mean length 15.0070922
Min length 3

Characters and Unicode

Total characters 2116
Distinct characters 27
Distinct categories 5 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 2 ?
Unique (%) 1.4%

Sample

1st row MSc/MA
2nd row MSc/MA
3rd row Undergraduate year 3
4th row Undergraduate year 1
5th row Undergraduate year 3

Common Values

Value Count Frequency (%)
MSc/MA 28
17.7%
Undergraduate year 1 28
17.7%
Undergraduate year 2 18
11.4%
Undergraduate year 3 14
8.9%
MSc/MA year 1 14
8.9%
MSc/MA year 2 14
8.9%
Undergraduate year 4 12
7.6%
BSc/BA year 1 5
3.2%
Other 2
1.3%
PhD 2
1.3%
Other values (3) 4
2.5%
(Missing) 17
10.8%

Length

2022-07-04T20:37:11.606976 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
year 109
30.2%
undergraduate 72
19.9%
msc/ma 56
15.5%
1 47
13.0%
2 34
9.4%
3 15
4.2%
4 13
3.6%
bsc/ba 9
2.5%
other 2
0.6%
phd 2
0.6%
Other values (2) 2
0.6%

Most occurring characters

Value Count Frequency (%)
e 256
12.1%
r 255
12.1%
a 254
12.0%
220
10.4%
d 146
6.9%
M 112
5.3%
y 110
5.2%
t 74
3.5%
n 74
3.5%
U 72
3.4%
Other values (17) 543
25.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1384
65.4%
Uppercase Letter 338
16.0%
Space Separator 220
10.4%
Decimal Number 109
5.2%
Other Punctuation 65
3.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 256
18.5%
r 255
18.4%
a 254
18.4%
d 146
10.5%
y 110
7.9%
t 74
5.3%
n 74
5.3%
g 72
5.2%
u 72
5.2%
c 65
4.7%
Other values (3) 6
0.4%
Uppercase Letter
Value Count Frequency (%)
M 112
33.1%
U 72
21.3%
A 65
19.2%
S 65
19.2%
B 18
5.3%
O 2
0.6%
P 2
0.6%
D 2
0.6%
Decimal Number
Value Count Frequency (%)
1 47
43.1%
2 34
31.2%
3 15
13.8%
4 13
11.9%
Space Separator
Value Count Frequency (%)
220
100.0%
Other Punctuation
Value Count Frequency (%)
/ 65
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1722
81.4%
Common 394
18.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 256
14.9%
r 255
14.8%
a 254
14.8%
d 146
8.5%
M 112
6.5%
y 110
6.4%
t 74
4.3%
n 74
4.3%
U 72
4.2%
g 72
4.2%
Other values (11) 297
17.2%
Common
Value Count Frequency (%)
220
55.8%
/ 65
16.5%
1 47
11.9%
2 34
8.6%
3 15
3.8%
4 13
3.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 2116
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 256
12.1%
r 255
12.1%
a 254
12.0%
220
10.4%
d 146
6.9%
M 112
5.3%
y 110
5.2%
t 74
3.5%
n 74
3.5%
U 72
3.4%
Other values (17) 543
25.7%

university
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.5%
Missing 16
Missing (%) 10.1%
Memory size 1.4 KiB
AAU
39
UNITN
33
NUM
28
UC
22
LSE
20

Length

Max length 5
Median length 3
Mean length 3.309859155
Min length 2

Characters and Unicode

Total characters 470
Distinct characters 10
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row UNITN
2nd row UNITN
3rd row UNITN
4th row UNITN
5th row UNITN

Common Values

Value Count Frequency (%)
AAU 39
24.7%
UNITN 33
20.9%
NUM 28
17.7%
UC 22
13.9%
LSE 20
12.7%
(Missing) 16
10.1%

Length

2022-07-04T20:37:12.051036 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:12.301069 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
aau 39
27.5%
unitn 33
23.2%
num 28
19.7%
uc 22
15.5%
lse 20
14.1%

Most occurring characters

Value Count Frequency (%)
U 122
26.0%
N 94
20.0%
A 78
16.6%
I 33
7.0%
T 33
7.0%
M 28
6.0%
C 22
4.7%
L 20
4.3%
S 20
4.3%
E 20
4.3%

Most occurring categories

Value Count Frequency (%)
Uppercase Letter 470
100.0%

Most frequent character per category

Uppercase Letter
Value Count Frequency (%)
U 122
26.0%
N 94
20.0%
A 78
16.6%
I 33
7.0%
T 33
7.0%
M 28
6.0%
C 22
4.7%
L 20
4.3%
S 20
4.3%
E 20
4.3%

Most occurring scripts

Value Count Frequency (%)
Latin 470
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
U 122
26.0%
N 94
20.0%
A 78
16.6%
I 33
7.0%
T 33
7.0%
M 28
6.0%
C 22
4.7%
L 20
4.3%
S 20
4.3%
E 20
4.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 470
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
U 122
26.0%
N 94
20.0%
A 78
16.6%
I 33
7.0%
T 33
7.0%
M 28
6.0%
C 22
4.7%
L 20
4.3%
S 20
4.3%
E 20
4.3%

accommodation
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 4
Distinct (%) 2.9%
Missing 20
Missing (%) 12.7%
Memory size 1.4 KiB
With family and/or relatives
59
Private shared accommodation
41
Hall of residence / dormitory
23
Other
15

Length

Max length 29
Median length 28
Mean length 25.66666667
Min length 5

Characters and Unicode

Total characters 3542
Distinct characters 22
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row With family and/or relatives
2nd row Private shared accommodation
3rd row With family and/or relatives
4th row Other
5th row Private shared accommodation

Common Values

Value Count Frequency (%)
With family and/or relatives 59
37.3%
Private shared accommodation 41
25.9%
Hall of residence / dormitory 23
14.6%
Other 15
9.5%
(Missing) 20
12.7%

Length

2022-07-04T20:37:12.542892 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:12.786762 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
with 59
12.1%
family 59
12.1%
and/or 59
12.1%
relatives 59
12.1%
private 41
8.4%
shared 41
8.4%
accommodation 41
8.4%
hall 23
4.7%
of 23
4.7%
residence 23
4.7%
Other values (3) 61
12.5%

Most occurring characters

Value Count Frequency (%)
a 364
10.3%
351
9.9%
i 305
8.6%
e 284
8.0%
r 284
8.0%
o 251
7.1%
t 238
6.7%
d 187
5.3%
m 164
4.6%
l 164
4.6%
Other values (12) 950
26.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2971
83.9%
Space Separator 351
9.9%
Uppercase Letter 138
3.9%
Other Punctuation 82
2.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 364
12.3%
i 305
10.3%
e 284
9.6%
r 284
9.6%
o 251
8.4%
t 238
8.0%
d 187
6.3%
m 164
5.5%
l 164
5.5%
s 123
4.1%
Other values (6) 607
20.4%
Uppercase Letter
Value Count Frequency (%)
W 59
42.8%
P 41
29.7%
H 23
16.7%
O 15
10.9%
Space Separator
Value Count Frequency (%)
351
100.0%
Other Punctuation
Value Count Frequency (%)
/ 82
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 3109
87.8%
Common 433
12.2%

Most frequent character per script

Latin
Value Count Frequency (%)
a 364
11.7%
i 305
9.8%
e 284
9.1%
r 284
9.1%
o 251
8.1%
t 238
7.7%
d 187
6.0%
m 164
5.3%
l 164
5.3%
s 123
4.0%
Other values (10) 745
24.0%
Common
Value Count Frequency (%)
351
81.1%
/ 82
18.9%

Most occurring blocks

Value Count Frequency (%)
ASCII 3542
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 364
10.3%
351
9.9%
i 305
8.6%
e 284
8.0%
r 284
8.0%
o 251
7.1%
t 238
6.7%
d 187
5.3%
m 164
4.6%
l 164
4.6%
Other values (12) 950
26.8%

excitement
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 11
Distinct (%) 7.6%
Missing 14
Missing (%) 8.9%
Infinite 0
Infinite (%) 0.0%
Mean 0.7101736111
Minimum 0.2
Maximum 1
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:13.020523 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0.2
5-th percentile 0.4769
Q1 0.6
median 0.733
Q3 0.8
95-th percentile 0.933
Maximum 1
Range 0.8
Interquartile range (IQR) 0.2

Descriptive statistics

Standard deviation 0.1397871174
Coefficient of variation (CV) 0.1968351332
Kurtosis 0.7381586259
Mean 0.7101736111
Median Absolute Deviation (MAD) 0.067
Skewness -0.3611535043
Sum 102.265
Variance 0.01954043818
Monotonicity Not monotonic
2022-07-04T20:37:13.223465 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
Value Count Frequency (%)
0.6 28
17.7%
0.8 27
17.1%
0.733 27
17.1%
0.667 20
12.7%
0.867 14
8.9%
0.533 9
5.7%
0.933 6
3.8%
1 5
3.2%
0.467 5
3.2%
0.333 2
1.3%
(Missing) 14
8.9%
Value Count Frequency (%)
0.2 1
0.6%
0.333 2
1.3%
0.467 5
3.2%
0.533 9
5.7%
0.6 28
17.7%
0.667 20
12.7%
0.733 27
17.1%
0.8 27
17.1%
0.867 14
8.9%
0.933 6
3.8%
Value Count Frequency (%)
1 5
3.2%
0.933 6
3.8%
0.867 14
8.9%
0.8 27
17.1%
0.733 27
17.1%
0.667 20
12.7%
0.6 28
17.7%
0.533 9
5.7%
0.467 5
3.2%
0.333 2
1.3%

promotion
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Distinct 12
Distinct (%) 8.4%
Missing 15
Missing (%) 9.5%
Infinite 0
Infinite (%) 0.0%
Mean 0.669951049
Minimum 0.267
Maximum 1
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:13.440965 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0.267
5-th percentile 0.4067
Q1 0.533
median 0.667
Q3 0.8
95-th percentile 0.933
Maximum 1
Range 0.733
Interquartile range (IQR) 0.267

Descriptive statistics

Standard deviation 0.1608550362
Coefficient of variation (CV) 0.2400996855
Kurtosis -0.4931861426
Mean 0.669951049
Median Absolute Deviation (MAD) 0.133
Skewness 0.06666627792
Sum 95.803
Variance 0.02587434266
Monotonicity Not monotonic
2022-07-04T20:37:13.637323 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
Value Count Frequency (%)
0.6 27
17.1%
0.667 22
13.9%
0.533 16
10.1%
0.733 16
10.1%
0.867 15
9.5%
0.8 14
8.9%
0.467 13
8.2%
0.933 7
4.4%
1 5
3.2%
0.4 4
2.5%
Other values (2) 4
2.5%
(Missing) 15
9.5%
Value Count Frequency (%)
0.267 1
0.6%
0.333 3
1.9%
0.4 4
2.5%
0.467 13
8.2%
0.533 16
10.1%
0.6 27
17.1%
0.667 22
13.9%
0.733 16
10.1%
0.8 14
8.9%
0.867 15
9.5%
Value Count Frequency (%)
1 5
3.2%
0.933 7
4.4%
0.867 15
9.5%
0.8 14
8.9%
0.733 16
10.1%
0.667 22
13.9%
0.6 27
17.1%
0.533 16
10.1%
0.467 13
8.2%
0.4 4
2.5%

existence
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Distinct 11
Distinct (%) 7.6%
Missing 14
Missing (%) 8.9%
Infinite 0
Infinite (%) 0.0%
Mean 0.8215277778
Minimum 0.4
Maximum 1
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:13.860172 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0.4
5-th percentile 0.6
Q1 0.733
median 0.867
Q3 0.933
95-th percentile 1
Maximum 1
Range 0.6
Interquartile range (IQR) 0.2

Descriptive statistics

Standard deviation 0.1349743278
Coefficient of variation (CV) 0.1642967303
Kurtosis 0.3766541517
Mean 0.8215277778
Median Absolute Deviation (MAD) 0.067
Skewness -0.787219052
Sum 118.3
Variance 0.01821806915
Monotonicity Not monotonic
2022-07-04T20:37:14.054353 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
Value Count Frequency (%)
0.867 31
19.6%
0.8 29
18.4%
0.933 22
13.9%
1 21
13.3%
0.733 15
9.5%
0.6 11
7.0%
0.667 8
5.1%
0.533 3
1.9%
0.4 2
1.3%
0.9 1
0.6%
(Missing) 14
8.9%
Value Count Frequency (%)
0.4 2
1.3%
0.467 1
0.6%
0.533 3
1.9%
0.6 11
7.0%
0.667 8
5.1%
0.733 15
9.5%
0.8 29
18.4%
0.867 31
19.6%
0.9 1
0.6%
0.933 22
13.9%
Value Count Frequency (%)
1 21
13.3%
0.933 22
13.9%
0.9 1
0.6%
0.867 31
19.6%
0.8 29
18.4%
0.733 15
9.5%
0.667 8
5.1%
0.6 11
7.0%
0.533 3
1.9%
0.467 1
0.6%

suprapersonal
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 9
Distinct (%) 6.2%
Missing 14
Missing (%) 8.9%
Infinite 0
Infinite (%) 0.0%
Mean 0.7921319444
Minimum 0.2
Maximum 1
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:14.268713 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0.2
5-th percentile 0.6
Q1 0.733
median 0.8
Q3 0.867
95-th percentile 1
Maximum 1
Range 0.8
Interquartile range (IQR) 0.134

Descriptive statistics

Standard deviation 0.1284497805
Coefficient of variation (CV) 0.1621570515
Kurtosis 1.884314792
Mean 0.7921319444
Median Absolute Deviation (MAD) 0.067
Skewness -0.6873011369
Sum 114.067
Variance 0.01649934611
Monotonicity Not monotonic
2022-07-04T20:37:14.476062 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
Value Count Frequency (%)
0.8 29
18.4%
0.867 27
17.1%
0.733 24
15.2%
0.667 18
11.4%
0.933 18
11.4%
0.6 13
8.2%
1 12
7.6%
0.533 2
1.3%
0.2 1
0.6%
(Missing) 14
8.9%
Value Count Frequency (%)
0.2 1
0.6%
0.533 2
1.3%
0.6 13
8.2%
0.667 18
11.4%
0.733 24
15.2%
0.8 29
18.4%
0.867 27
17.1%
0.933 18
11.4%
1 12
7.6%
Value Count Frequency (%)
1 12
7.6%
0.933 18
11.4%
0.867 27
17.1%
0.8 29
18.4%
0.733 24
15.2%
0.667 18
11.4%
0.6 13
8.2%
0.533 2
1.3%
0.2 1
0.6%

interactive
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Distinct 10
Distinct (%) 7.0%
Missing 15
Missing (%) 9.5%
Infinite 0
Infinite (%) 0.0%
Mean 0.7985734266
Minimum 0.2
Maximum 1
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:14.702309 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0.2
5-th percentile 0.533
Q1 0.733
median 0.8
Q3 0.933
95-th percentile 1
Maximum 1
Range 0.8
Interquartile range (IQR) 0.2

Descriptive statistics

Standard deviation 0.1425145157
Coefficient of variation (CV) 0.1784613799
Kurtosis 1.557535184
Mean 0.7985734266
Median Absolute Deviation (MAD) 0.133
Skewness -0.9081898254
Sum 114.196
Variance 0.02031038718
Monotonicity Not monotonic
2022-07-04T20:37:14.911810 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
Value Count Frequency (%)
0.8 26
16.5%
0.867 25
15.8%
0.933 25
15.8%
0.733 20
12.7%
0.667 16
10.1%
1 14
8.9%
0.6 8
5.1%
0.533 7
4.4%
0.333 1
0.6%
0.2 1
0.6%
(Missing) 15
9.5%
Value Count Frequency (%)
0.2 1
0.6%
0.333 1
0.6%
0.533 7
4.4%
0.6 8
5.1%
0.667 16
10.1%
0.733 20
12.7%
0.8 26
16.5%
0.867 25
15.8%
0.933 25
15.8%
1 14
8.9%
Value Count Frequency (%)
1 14
8.9%
0.933 25
15.8%
0.867 25
15.8%
0.8 26
16.5%
0.733 20
12.7%
0.667 16
10.1%
0.6 8
5.1%
0.533 7
4.4%
0.333 1
0.6%
0.2 1
0.6%

normative
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Distinct 14
Distinct (%) 9.7%
Missing 14
Missing (%) 8.9%
Infinite 0
Infinite (%) 0.0%
Mean 0.5451388889
Minimum 0.2
Maximum 1
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:15.153969 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0.2
5-th percentile 0.267
Q1 0.4
median 0.533
Q3 0.667
95-th percentile 0.867
Maximum 1
Range 0.8
Interquartile range (IQR) 0.267

Descriptive statistics

Standard deviation 0.1862688714
Coefficient of variation (CV) 0.3416906686
Kurtosis -0.6473984377
Mean 0.5451388889
Median Absolute Deviation (MAD) 0.134
Skewness 0.2330785683
Sum 78.5
Variance 0.03469609246
Monotonicity Not monotonic
2022-07-04T20:37:15.371742 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
Value Count Frequency (%)
0.6 20
12.7%
0.467 18
11.4%
0.533 16
10.1%
0.333 15
9.5%
0.4 14
8.9%
0.733 14
8.9%
0.267 13
8.2%
0.667 13
8.2%
0.8 9
5.7%
0.867 4
2.5%
Other values (4) 8
5.1%
(Missing) 14
8.9%
Value Count Frequency (%)
0.2 2
1.3%
0.267 13
8.2%
0.333 15
9.5%
0.4 14
8.9%
0.467 18
11.4%
0.533 16
10.1%
0.6 20
12.7%
0.667 13
8.2%
0.7 1
0.6%
0.733 14
8.9%
Value Count Frequency (%)
1 2
1.3%
0.933 3
1.9%
0.867 4
2.5%
0.8 9
5.7%
0.733 14
8.9%
0.7 1
0.6%
0.667 13
8.2%
0.6 20
12.7%
0.533 16
10.1%
0.467 18
11.4%

extraversion
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Distinct 17
Distinct (%) 11.8%
Missing 14
Missing (%) 8.9%
Infinite 0
Infinite (%) 0.0%
Mean 0.5493055556
Minimum 0.2
Maximum 1
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:15.622251 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0.2
5-th percentile 0.2575
Q1 0.4
median 0.55
Q3 0.7
95-th percentile 0.9
Maximum 1
Range 0.8
Interquartile range (IQR) 0.3

Descriptive statistics

Standard deviation 0.1939636358
Coefficient of variation (CV) 0.3531069981
Kurtosis -0.5401187554
Mean 0.5493055556
Median Absolute Deviation (MAD) 0.15
Skewness 0.3451915768
Sum 79.1
Variance 0.037621892
Monotonicity Not monotonic
2022-07-04T20:37:15.847914 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
Value Count Frequency (%)
0.55 16
10.1%
0.45 16
10.1%
0.5 16
10.1%
0.35 12
7.6%
0.6 12
7.6%
0.3 10
6.3%
0.7 10
6.3%
0.4 9
5.7%
0.8 8
5.1%
0.75 7
4.4%
Other values (7) 28
17.7%
(Missing) 14
8.9%
Value Count Frequency (%)
0.2 5
3.2%
0.25 3
1.9%
0.3 10
6.3%
0.35 12
7.6%
0.4 9
5.7%
0.45 16
10.1%
0.5 16
10.1%
0.55 16
10.1%
0.6 12
7.6%
0.65 6
3.8%
Value Count Frequency (%)
1 2
1.3%
0.95 4
2.5%
0.9 4
2.5%
0.85 4
2.5%
0.8 8
5.1%
0.75 7
4.4%
0.7 10
6.3%
0.65 6
3.8%
0.6 12
7.6%
0.55 16
10.1%

agreeableness
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Distinct 15
Distinct (%) 10.4%
Missing 14
Missing (%) 8.9%
Infinite 0
Infinite (%) 0.0%
Mean 0.7745347222
Minimum 0.35
Maximum 1
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:16.081050 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0.35
5-th percentile 0.55
Q1 0.65
median 0.8
Q3 0.9
95-th percentile 1
Maximum 1
Range 0.65
Interquartile range (IQR) 0.25

Descriptive statistics

Standard deviation 0.1441422138
Coefficient of variation (CV) 0.186101681
Kurtosis -0.1443215138
Mean 0.7745347222
Median Absolute Deviation (MAD) 0.1
Skewness -0.4183913804
Sum 111.533
Variance 0.02077697781
Monotonicity Not monotonic
2022-07-04T20:37:16.279404 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
Value Count Frequency (%)
0.8 25
15.8%
0.75 18
11.4%
0.65 17
10.8%
0.95 15
9.5%
0.9 15
9.5%
0.7 11
7.0%
1 11
7.0%
0.85 10
6.3%
0.6 10
6.3%
0.55 5
3.2%
Other values (5) 7
4.4%
(Missing) 14
8.9%
Value Count Frequency (%)
0.35 1
0.6%
0.4 2
1.3%
0.45 2
1.3%
0.5 1
0.6%
0.55 5
3.2%
0.6 10
6.3%
0.65 17
10.8%
0.7 11
7.0%
0.733 1
0.6%
0.75 18
11.4%
Value Count Frequency (%)
1 11
7.0%
0.95 15
9.5%
0.9 15
9.5%
0.85 10
6.3%
0.8 25
15.8%
0.75 18
11.4%
0.733 1
0.6%
0.7 11
7.0%
0.65 17
10.8%
0.6 10
6.3%

conscientiousness
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Distinct 16
Distinct (%) 11.1%
Missing 14
Missing (%) 8.9%
Infinite 0
Infinite (%) 0.0%
Mean 0.6960625
Minimum 0.2
Maximum 1
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:16.502184 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0.2
5-th percentile 0.45
Q1 0.6
median 0.7
Q3 0.8
95-th percentile 0.95
Maximum 1
Range 0.8
Interquartile range (IQR) 0.2

Descriptive statistics

Standard deviation 0.1516956937
Coefficient of variation (CV) 0.2179340126
Kurtosis -0.1187464982
Mean 0.6960625
Median Absolute Deviation (MAD) 0.1
Skewness -0.2387585511
Sum 100.233
Variance 0.02301158348
Monotonicity Not monotonic
2022-07-04T20:37:16.716476 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
Value Count Frequency (%)
0.8 22
13.9%
0.65 18
11.4%
0.6 17
10.8%
0.7 17
10.8%
0.85 14
8.9%
0.55 12
7.6%
0.75 9
5.7%
0.95 9
5.7%
0.45 8
5.1%
0.5 7
4.4%
Other values (6) 11
7.0%
(Missing) 14
8.9%
Value Count Frequency (%)
0.2 1
0.6%
0.3 1
0.6%
0.4 1
0.6%
0.45 8
5.1%
0.5 7
4.4%
0.533 1
0.6%
0.55 12
7.6%
0.6 17
10.8%
0.65 18
11.4%
0.7 17
10.8%
Value Count Frequency (%)
1 2
1.3%
0.95 9
5.7%
0.9 5
3.2%
0.85 14
8.9%
0.8 22
13.9%
0.75 9
5.7%
0.7 17
10.8%
0.65 18
11.4%
0.6 17
10.8%
0.55 12
7.6%

neuroticism
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Distinct 16
Distinct (%) 11.1%
Missing 14
Missing (%) 8.9%
Infinite 0
Infinite (%) 0.0%
Mean 0.6083333333
Minimum 0.2
Maximum 1
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:16.952013 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0.2
5-th percentile 0.35
Q1 0.5
median 0.6
Q3 0.75
95-th percentile 0.8925
Maximum 1
Range 0.8
Interquartile range (IQR) 0.25

Descriptive statistics

Standard deviation 0.1663049688
Coefficient of variation (CV) 0.273378031
Kurtosis -0.2409018667
Mean 0.6083333333
Median Absolute Deviation (MAD) 0.1
Skewness 0.06250633472
Sum 87.6
Variance 0.02765734266
Monotonicity Not monotonic
2022-07-04T20:37:17.172964 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
Value Count Frequency (%)
0.6 23
14.6%
0.55 16
10.1%
0.75 15
9.5%
0.5 14
8.9%
0.7 12
7.6%
0.65 12
7.6%
0.4 10
6.3%
0.45 9
5.7%
0.85 8
5.1%
0.8 6
3.8%
Other values (6) 19
12.0%
(Missing) 14
8.9%
Value Count Frequency (%)
0.2 2
1.3%
0.3 4
2.5%
0.35 5
3.2%
0.4 10
6.3%
0.45 9
5.7%
0.5 14
8.9%
0.55 16
10.1%
0.6 23
14.6%
0.65 12
7.6%
0.7 12
7.6%
Value Count Frequency (%)
1 3
1.9%
0.95 1
0.6%
0.9 4
2.5%
0.85 8
5.1%
0.8 6
3.8%
0.75 15
9.5%
0.7 12
7.6%
0.65 12
7.6%
0.6 23
14.6%
0.55 16
10.1%

openness
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Distinct 14
Distinct (%) 9.7%
Missing 14
Missing (%) 8.9%
Infinite 0
Infinite (%) 0.0%
Mean 0.7587986111
Minimum 0.25
Maximum 1
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.4 KiB
2022-07-04T20:37:17.596620 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0.25
5-th percentile 0.55
Q1 0.65
median 0.75
Q3 0.85
95-th percentile 1
Maximum 1
Range 0.75
Interquartile range (IQR) 0.2

Descriptive statistics

Standard deviation 0.1453243577
Coefficient of variation (CV) 0.1915190086
Kurtosis -0.1467169661
Mean 0.7587986111
Median Absolute Deviation (MAD) 0.1
Skewness -0.2243304974
Sum 109.267
Variance 0.02111916895
Monotonicity Not monotonic
2022-07-04T20:37:17.795569 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
Value Count Frequency (%)
0.65 18
11.4%
0.7 16
10.1%
0.85 16
10.1%
0.75 16
10.1%
0.8 15
9.5%
0.6 14
8.9%
0.95 14
8.9%
0.55 11
7.0%
1 10
6.3%
0.9 10
6.3%
Other values (4) 4
2.5%
(Missing) 14
8.9%
Value Count Frequency (%)
0.25 1
0.6%
0.4 1
0.6%
0.5 1
0.6%
0.55 11
7.0%
0.6 14
8.9%
0.65 18
11.4%
0.7 16
10.1%
0.75 16
10.1%
0.8 15
9.5%
0.85 16
10.1%
Value Count Frequency (%)
1 10
6.3%
0.95 14
8.9%
0.9 10
6.3%
0.867 1
0.6%
0.85 16
10.1%
0.8 15
9.5%
0.75 16
10.1%
0.7 16
10.1%
0.65 18
11.4%
0.6 14
8.9%

c_food
Categorical

HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 12
Missing (%) 7.6%
Memory size 1.4 KiB
0.75
50
0.5
37
1.0
32
0.25
19
0.0
8

Length

Max length 4
Median length 3
Mean length 3.47260274
Min length 3

Characters and Unicode

Total characters 507
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.25
2nd row 0.25
3rd row 0.25
4th row 0.0
5th row 0.75

Common Values

Value Count Frequency (%)
0.75 50
31.6%
0.5 37
23.4%
1.0 32
20.3%
0.25 19
12.0%
0.0 8
5.1%
(Missing) 12
7.6%

Length

2022-07-04T20:37:18.038566 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:18.292032 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.75 50
34.2%
0.5 37
25.3%
1.0 32
21.9%
0.25 19
13.0%
0.0 8
5.5%

Most occurring characters

Value Count Frequency (%)
0 154
30.4%
. 146
28.8%
5 106
20.9%
7 50
9.9%
1 32
6.3%
2 19
3.7%

Most occurring categories

Value Count Frequency (%)
Decimal Number 361
71.2%
Other Punctuation 146
28.8%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 154
42.7%
5 106
29.4%
7 50
13.9%
1 32
8.9%
2 19
5.3%
Other Punctuation
Value Count Frequency (%)
. 146
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 507
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 154
30.4%
. 146
28.8%
5 106
20.9%
7 50
9.9%
1 32
6.3%
2 19
3.7%

Most occurring blocks

Value Count Frequency (%)
ASCII 507
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 154
30.4%
. 146
28.8%
5 106
20.9%
7 50
9.9%
1 32
6.3%
2 19
3.7%

c_eating
Categorical

HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 12
Missing (%) 7.6%
Memory size 1.4 KiB
0.75
51
0.5
45
0.25
25
1.0
15
0.0
10

Length

Max length 4
Median length 4
Mean length 3.520547945
Min length 3

Characters and Unicode

Total characters 514
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.5
2nd row 0.75
3rd row 0.25
4th row 0.0
5th row 0.5

Common Values

Value Count Frequency (%)
0.75 51
32.3%
0.5 45
28.5%
0.25 25
15.8%
1.0 15
9.5%
0.0 10
6.3%
(Missing) 12
7.6%

Length

2022-07-04T20:37:18.522506 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:18.771260 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.75 51
34.9%
0.5 45
30.8%
0.25 25
17.1%
1.0 15
10.3%
0.0 10
6.8%

Most occurring characters

Value Count Frequency (%)
0 156
30.4%
. 146
28.4%
5 121
23.5%
7 51
9.9%
2 25
4.9%
1 15
2.9%

Most occurring categories

Value Count Frequency (%)
Decimal Number 368
71.6%
Other Punctuation 146
28.4%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 156
42.4%
5 121
32.9%
7 51
13.9%
2 25
6.8%
1 15
4.1%
Other Punctuation
Value Count Frequency (%)
. 146
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 514
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 156
30.4%
. 146
28.4%
5 121
23.5%
7 51
9.9%
2 25
4.9%
1 15
2.9%

Most occurring blocks

Value Count Frequency (%)
ASCII 514
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 156
30.4%
. 146
28.4%
5 121
23.5%
7 51
9.9%
2 25
4.9%
1 15
2.9%

c_lit
Categorical

HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.5
53
0.75
40
0.25
24
1.0
24
0.0
4

Length

Max length 4
Median length 3
Mean length 3.44137931
Min length 3

Characters and Unicode

Total characters 499
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.25
2nd row 0.5
3rd row 0.25
4th row 1.0
5th row 0.5

Common Values

Value Count Frequency (%)
0.5 53
33.5%
0.75 40
25.3%
0.25 24
15.2%
1.0 24
15.2%
0.0 4
2.5%
(Missing) 13
8.2%

Length

2022-07-04T20:37:19.004510 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:19.259472 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.5 53
36.6%
0.75 40
27.6%
0.25 24
16.6%
1.0 24
16.6%
0.0 4
2.8%

Most occurring characters

Value Count Frequency (%)
0 149
29.9%
. 145
29.1%
5 117
23.4%
7 40
8.0%
2 24
4.8%
1 24
4.8%

Most occurring categories

Value Count Frequency (%)
Decimal Number 354
70.9%
Other Punctuation 145
29.1%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 149
42.1%
5 117
33.1%
7 40
11.3%
2 24
6.8%
1 24
6.8%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 499
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 149
29.9%
. 145
29.1%
5 117
23.4%
7 40
8.0%
2 24
4.8%
1 24
4.8%

Most occurring blocks

Value Count Frequency (%)
ASCII 499
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 149
29.9%
. 145
29.1%
5 117
23.4%
7 40
8.0%
2 24
4.8%
1 24
4.8%

c_creatlit
Categorical

HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.25
42
0.5
36
0.0
31
0.75
28
1.0
8

Length

Max length 4
Median length 3
Mean length 3.482758621
Min length 3

Characters and Unicode

Total characters 505
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.25
2nd row 0.0
3rd row 0.0
4th row 1.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.25 42
26.6%
0.5 36
22.8%
0.0 31
19.6%
0.75 28
17.7%
1.0 8
5.1%
(Missing) 13
8.2%

Length

2022-07-04T20:37:19.489256 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:19.739578 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.25 42
29.0%
0.5 36
24.8%
0.0 31
21.4%
0.75 28
19.3%
1.0 8
5.5%

Most occurring characters

Value Count Frequency (%)
0 176
34.9%
. 145
28.7%
5 106
21.0%
2 42
8.3%
7 28
5.5%
1 8
1.6%

Most occurring categories

Value Count Frequency (%)
Decimal Number 360
71.3%
Other Punctuation 145
28.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 176
48.9%
5 106
29.4%
2 42
11.7%
7 28
7.8%
1 8
2.2%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 505
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 176
34.9%
. 145
28.7%
5 106
21.0%
2 42
8.3%
7 28
5.5%
1 8
1.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 505
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 176
34.9%
. 145
28.7%
5 106
21.0%
2 42
8.3%
7 28
5.5%
1 8
1.6%

c_perf_mus
Categorical

HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.5%
Missing 14
Missing (%) 8.9%
Memory size 1.4 KiB
0.0
55
0.25
31
0.75
23
0.5
21
1.0
14

Length

Max length 4
Median length 3
Mean length 3.375
Min length 3

Characters and Unicode

Total characters 486
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.25
3rd row 0.0
4th row 0.0
5th row 0.75

Common Values

Value Count Frequency (%)
0.0 55
34.8%
0.25 31
19.6%
0.75 23
14.6%
0.5 21
13.3%
1.0 14
8.9%
(Missing) 14
8.9%

Length

2022-07-04T20:37:19.970527 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:20.214900 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.0 55
38.2%
0.25 31
21.5%
0.75 23
16.0%
0.5 21
14.6%
1.0 14
9.7%

Most occurring characters

Value Count Frequency (%)
0 199
40.9%
. 144
29.6%
5 75
15.4%
2 31
6.4%
7 23
4.7%
1 14
2.9%

Most occurring categories

Value Count Frequency (%)
Decimal Number 342
70.4%
Other Punctuation 144
29.6%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 199
58.2%
5 75
21.9%
2 31
9.1%
7 23
6.7%
1 14
4.1%
Other Punctuation
Value Count Frequency (%)
. 144
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 486
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 199
40.9%
. 144
29.6%
5 75
15.4%
2 31
6.4%
7 23
4.7%
1 14
2.9%

Most occurring blocks

Value Count Frequency (%)
ASCII 486
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 199
40.9%
. 144
29.6%
5 75
15.4%
2 31
6.4%
7 23
4.7%
1 14
2.9%

c_plays
Categorical

HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.5%
Missing 14
Missing (%) 8.9%
Memory size 1.4 KiB
0.5
50
0.75
44
1.0
33
0.25
14
0.0
3

Length

Max length 4
Median length 3
Mean length 3.402777778
Min length 3

Characters and Unicode

Total characters 490
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.75
2nd row 0.5
3rd row 0.5
4th row 1.0
5th row 0.75

Common Values

Value Count Frequency (%)
0.5 50
31.6%
0.75 44
27.8%
1.0 33
20.9%
0.25 14
8.9%
0.0 3
1.9%
(Missing) 14
8.9%

Length

2022-07-04T20:37:20.443085 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:20.686217 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.5 50
34.7%
0.75 44
30.6%
1.0 33
22.9%
0.25 14
9.7%
0.0 3
2.1%

Most occurring characters

Value Count Frequency (%)
0 147
30.0%
. 144
29.4%
5 108
22.0%
7 44
9.0%
1 33
6.7%
2 14
2.9%

Most occurring categories

Value Count Frequency (%)
Decimal Number 346
70.6%
Other Punctuation 144
29.4%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 147
42.5%
5 108
31.2%
7 44
12.7%
1 33
9.5%
2 14
4.0%
Other Punctuation
Value Count Frequency (%)
. 144
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 490
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 147
30.0%
. 144
29.4%
5 108
22.0%
7 44
9.0%
1 33
6.7%
2 14
2.9%

Most occurring blocks

Value Count Frequency (%)
ASCII 490
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 147
30.0%
. 144
29.4%
5 108
22.0%
7 44
9.0%
1 33
6.7%
2 14
2.9%

c_perf_plays
Categorical

HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.5%
Missing 14
Missing (%) 8.9%
Memory size 1.4 KiB
0.0
49
0.25
43
0.5
25
0.75
20
1.0
7

Length

Max length 4
Median length 3
Mean length 3.4375
Min length 3

Characters and Unicode

Total characters 495
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.5
3rd row 0.0
4th row 1.0
5th row 0.25

Common Values

Value Count Frequency (%)
0.0 49
31.0%
0.25 43
27.2%
0.5 25
15.8%
0.75 20
12.7%
1.0 7
4.4%
(Missing) 14
8.9%

Length

2022-07-04T20:37:20.944573 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:21.202059 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.0 49
34.0%
0.25 43
29.9%
0.5 25
17.4%
0.75 20
13.9%
1.0 7
4.9%

Most occurring characters

Value Count Frequency (%)
0 193
39.0%
. 144
29.1%
5 88
17.8%
2 43
8.7%
7 20
4.0%
1 7
1.4%

Most occurring categories

Value Count Frequency (%)
Decimal Number 351
70.9%
Other Punctuation 144
29.1%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 193
55.0%
5 88
25.1%
2 43
12.3%
7 20
5.7%
1 7
2.0%
Other Punctuation
Value Count Frequency (%)
. 144
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 495
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 193
39.0%
. 144
29.1%
5 88
17.8%
2 43
8.7%
7 20
4.0%
1 7
1.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 193
39.0%
. 144
29.1%
5 88
17.8%
2 43
8.7%
7 20
4.0%
1 7
1.4%

c_musgall
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.5
45
0.25
34
0.75
30
1.0
18
0.0
18

Length

Max length 4
Median length 3
Mean length 3.44137931
Min length 3

Characters and Unicode

Total characters 499
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.5
2nd row 0.25
3rd row 0.25
4th row 1.0
5th row 0.25

Common Values

Value Count Frequency (%)
0.5 45
28.5%
0.25 34
21.5%
0.75 30
19.0%
1.0 18
11.4%
0.0 18
11.4%
(Missing) 13
8.2%

Length

2022-07-04T20:37:21.431426 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:21.684422 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.5 45
31.0%
0.25 34
23.4%
0.75 30
20.7%
1.0 18
12.4%
0.0 18
12.4%

Most occurring characters

Value Count Frequency (%)
0 163
32.7%
. 145
29.1%
5 109
21.8%
2 34
6.8%
7 30
6.0%
1 18
3.6%

Most occurring categories

Value Count Frequency (%)
Decimal Number 354
70.9%
Other Punctuation 145
29.1%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 163
46.0%
5 109
30.8%
2 34
9.6%
7 30
8.5%
1 18
5.1%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 499
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 163
32.7%
. 145
29.1%
5 109
21.8%
2 34
6.8%
7 30
6.0%
1 18
3.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 499
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 163
32.7%
. 145
29.1%
5 109
21.8%
2 34
6.8%
7 30
6.0%
1 18
3.6%

c_perf_art
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.0
38
0.25
33
0.75
30
0.5
23
1.0
21

Length

Max length 4
Median length 3
Mean length 3.434482759
Min length 3

Characters and Unicode

Total characters 498
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.25
2nd row 0.25
3rd row 0.0
4th row 1.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 38
24.1%
0.25 33
20.9%
0.75 30
19.0%
0.5 23
14.6%
1.0 21
13.3%
(Missing) 13
8.2%

Length

2022-07-04T20:37:21.928490 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:22.182214 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.0 38
26.2%
0.25 33
22.8%
0.75 30
20.7%
0.5 23
15.9%
1.0 21
14.5%

Most occurring characters

Value Count Frequency (%)
0 183
36.7%
. 145
29.1%
5 86
17.3%
2 33
6.6%
7 30
6.0%
1 21
4.2%

Most occurring categories

Value Count Frequency (%)
Decimal Number 353
70.9%
Other Punctuation 145
29.1%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 183
51.8%
5 86
24.4%
2 33
9.3%
7 30
8.5%
1 21
5.9%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 498
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 183
36.7%
. 145
29.1%
5 86
17.3%
2 33
6.6%
7 30
6.0%
1 21
4.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 498
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 183
36.7%
. 145
29.1%
5 86
17.3%
2 33
6.6%
7 30
6.0%
1 21
4.2%

c_watch_sp
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.5%
Missing 14
Missing (%) 8.9%
Memory size 1.4 KiB
0.0
52
0.25
39
0.5
23
0.75
17
1.0
13

Length

Max length 4
Median length 3
Mean length 3.388888889
Min length 3

Characters and Unicode

Total characters 488
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.25
2nd row 0.75
3rd row 0.0
4th row 0.0
5th row 1.0

Common Values

Value Count Frequency (%)
0.0 52
32.9%
0.25 39
24.7%
0.5 23
14.6%
0.75 17
10.8%
1.0 13
8.2%
(Missing) 14
8.9%

Length

2022-07-04T20:37:22.411317 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:22.653108 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.0 52
36.1%
0.25 39
27.1%
0.5 23
16.0%
0.75 17
11.8%
1.0 13
9.0%

Most occurring characters

Value Count Frequency (%)
0 196
40.2%
. 144
29.5%
5 79
16.2%
2 39
8.0%
7 17
3.5%
1 13
2.7%

Most occurring categories

Value Count Frequency (%)
Decimal Number 344
70.5%
Other Punctuation 144
29.5%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 196
57.0%
5 79
23.0%
2 39
11.3%
7 17
4.9%
1 13
3.8%
Other Punctuation
Value Count Frequency (%)
. 144
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 488
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 196
40.2%
. 144
29.5%
5 79
16.2%
2 39
8.0%
7 17
3.5%
1 13
2.7%

Most occurring blocks

Value Count Frequency (%)
ASCII 488
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 196
40.2%
. 144
29.5%
5 79
16.2%
2 39
8.0%
7 17
3.5%
1 13
2.7%

c_ind_sp
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.25
40
0.5
36
0.0
33
1.0
18
0.75
18

Length

Max length 4
Median length 3
Mean length 3.4
Min length 3

Characters and Unicode

Total characters 493
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1.0
2nd row 0.5
3rd row 0.5
4th row 1.0
5th row 1.0

Common Values

Value Count Frequency (%)
0.25 40
25.3%
0.5 36
22.8%
0.0 33
20.9%
1.0 18
11.4%
0.75 18
11.4%
(Missing) 13
8.2%

Length

2022-07-04T20:37:22.884758 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:23.126026 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.25 40
27.6%
0.5 36
24.8%
0.0 33
22.8%
1.0 18
12.4%
0.75 18
12.4%

Most occurring characters

Value Count Frequency (%)
0 178
36.1%
. 145
29.4%
5 94
19.1%
2 40
8.1%
1 18
3.7%
7 18
3.7%

Most occurring categories

Value Count Frequency (%)
Decimal Number 348
70.6%
Other Punctuation 145
29.4%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 178
51.1%
5 94
27.0%
2 40
11.5%
1 18
5.2%
7 18
5.2%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 493
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 178
36.1%
. 145
29.4%
5 94
19.1%
2 40
8.1%
1 18
3.7%
7 18
3.7%

Most occurring blocks

Value Count Frequency (%)
ASCII 493
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 178
36.1%
. 145
29.4%
5 94
19.1%
2 40
8.1%
1 18
3.7%
7 18
3.7%

c_team_sp
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.5%
Missing 14
Missing (%) 8.9%
Memory size 1.4 KiB
0.0
45
0.5
32
0.25
31
1.0
19
0.75
17

Length

Max length 4
Median length 3
Mean length 3.333333333
Min length 3

Characters and Unicode

Total characters 480
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1.0
2nd row 0.75
3rd row 0.0
4th row 0.0
5th row 1.0

Common Values

Value Count Frequency (%)
0.0 45
28.5%
0.5 32
20.3%
0.25 31
19.6%
1.0 19
12.0%
0.75 17
10.8%
(Missing) 14
8.9%

Length

2022-07-04T20:37:23.548931 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:23.793733 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.0 45
31.2%
0.5 32
22.2%
0.25 31
21.5%
1.0 19
13.2%
0.75 17
11.8%

Most occurring characters

Value Count Frequency (%)
0 189
39.4%
. 144
30.0%
5 80
16.7%
2 31
6.5%
1 19
4.0%
7 17
3.5%

Most occurring categories

Value Count Frequency (%)
Decimal Number 336
70.0%
Other Punctuation 144
30.0%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 189
56.2%
5 80
23.8%
2 31
9.2%
1 19
5.7%
7 17
5.1%
Other Punctuation
Value Count Frequency (%)
. 144
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 480
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 189
39.4%
. 144
30.0%
5 80
16.7%
2 31
6.5%
1 19
4.0%
7 17
3.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 480
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 189
39.4%
. 144
30.0%
5 80
16.7%
2 31
6.5%
1 19
4.0%
7 17
3.5%

c_accom
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.5
40
0.75
36
0.25
31
0.0
21
1.0
17

Length

Max length 4
Median length 3
Mean length 3.462068966
Min length 3

Characters and Unicode

Total characters 502
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.5
2nd row 0.25
3rd row 0.0
4th row 0.0
5th row 0.25

Common Values

Value Count Frequency (%)
0.5 40
25.3%
0.75 36
22.8%
0.25 31
19.6%
0.0 21
13.3%
1.0 17
10.8%
(Missing) 13
8.2%

Length

2022-07-04T20:37:24.035468 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:24.291645 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.5 40
27.6%
0.75 36
24.8%
0.25 31
21.4%
0.0 21
14.5%
1.0 17
11.7%

Most occurring characters

Value Count Frequency (%)
0 166
33.1%
. 145
28.9%
5 107
21.3%
7 36
7.2%
2 31
6.2%
1 17
3.4%

Most occurring categories

Value Count Frequency (%)
Decimal Number 357
71.1%
Other Punctuation 145
28.9%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 166
46.5%
5 107
30.0%
7 36
10.1%
2 31
8.7%
1 17
4.8%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 502
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 166
33.1%
. 145
28.9%
5 107
21.3%
7 36
7.2%
2 31
6.2%
1 17
3.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 502
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 166
33.1%
. 145
28.9%
5 107
21.3%
7 36
7.2%
2 31
6.2%
1 17
3.4%

c_locfac
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.75
45
0.5
43
1.0
29
0.25
19
0.0
9

Length

Max length 4
Median length 3
Mean length 3.44137931
Min length 3

Characters and Unicode

Total characters 499
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.5
2nd row 0.5
3rd row 0.25
4th row 1.0
5th row 0.75

Common Values

Value Count Frequency (%)
0.75 45
28.5%
0.5 43
27.2%
1.0 29
18.4%
0.25 19
12.0%
0.0 9
5.7%
(Missing) 13
8.2%

Length

2022-07-04T20:37:24.527022 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:24.771743 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.75 45
31.0%
0.5 43
29.7%
1.0 29
20.0%
0.25 19
13.1%
0.0 9
6.2%

Most occurring characters

Value Count Frequency (%)
0 154
30.9%
. 145
29.1%
5 107
21.4%
7 45
9.0%
1 29
5.8%
2 19
3.8%

Most occurring categories

Value Count Frequency (%)
Decimal Number 354
70.9%
Other Punctuation 145
29.1%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 154
43.5%
5 107
30.2%
7 45
12.7%
1 29
8.2%
2 19
5.4%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 499
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 154
30.9%
. 145
29.1%
5 107
21.4%
7 45
9.0%
1 29
5.8%
2 19
3.8%

Most occurring blocks

Value Count Frequency (%)
ASCII 499
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 154
30.9%
. 145
29.1%
5 107
21.4%
7 45
9.0%
1 29
5.8%
2 19
3.8%

u_active
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.75
48
0.5
47
1.0
32
0.25
15
0.0
3

Length

Max length 4
Median length 3
Mean length 3.434482759
Min length 3

Characters and Unicode

Total characters 498
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.5
2nd row 0.5
3rd row 0.25
4th row 0.0
5th row 0.5

Common Values

Value Count Frequency (%)
0.75 48
30.4%
0.5 47
29.7%
1.0 32
20.3%
0.25 15
9.5%
0.0 3
1.9%
(Missing) 13
8.2%

Length

2022-07-04T20:37:24.999458 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:25.248901 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.75 48
33.1%
0.5 47
32.4%
1.0 32
22.1%
0.25 15
10.3%
0.0 3
2.1%

Most occurring characters

Value Count Frequency (%)
0 148
29.7%
. 145
29.1%
5 110
22.1%
7 48
9.6%
1 32
6.4%
2 15
3.0%

Most occurring categories

Value Count Frequency (%)
Decimal Number 353
70.9%
Other Punctuation 145
29.1%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 148
41.9%
5 110
31.2%
7 48
13.6%
1 32
9.1%
2 15
4.2%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 498
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 148
29.7%
. 145
29.1%
5 110
22.1%
7 48
9.6%
1 32
6.4%
2 15
3.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 498
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 148
29.7%
. 145
29.1%
5 110
22.1%
7 48
9.6%
1 32
6.4%
2 15
3.0%

u_read
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.75
51
0.5
37
0.25
25
1.0
23
0.0
9

Length

Max length 4
Median length 4
Mean length 3.524137931
Min length 3

Characters and Unicode

Total characters 511
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.75
2nd row 0.25
3rd row 0.0
4th row 0.0
5th row 0.5

Common Values

Value Count Frequency (%)
0.75 51
32.3%
0.5 37
23.4%
0.25 25
15.8%
1.0 23
14.6%
0.0 9
5.7%
(Missing) 13
8.2%

Length

2022-07-04T20:37:25.476333 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:25.726548 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.75 51
35.2%
0.5 37
25.5%
0.25 25
17.2%
1.0 23
15.9%
0.0 9
6.2%

Most occurring characters

Value Count Frequency (%)
0 154
30.1%
. 145
28.4%
5 113
22.1%
7 51
10.0%
2 25
4.9%
1 23
4.5%

Most occurring categories

Value Count Frequency (%)
Decimal Number 366
71.6%
Other Punctuation 145
28.4%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 154
42.1%
5 113
30.9%
7 51
13.9%
2 25
6.8%
1 23
6.3%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 511
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 154
30.1%
. 145
28.4%
5 113
22.1%
7 51
10.0%
2 25
4.9%
1 23
4.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 511
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 154
30.1%
. 145
28.4%
5 113
22.1%
7 51
10.0%
2 25
4.9%
1 23
4.5%

u_essay
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.5
43
0.25
34
0.75
33
0.0
19
1.0
16

Length

Max length 4
Median length 3
Mean length 3.462068966
Min length 3

Characters and Unicode

Total characters 502
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.25
2nd row 0.25
3rd row 0.0
4th row 0.0
5th row 0.25

Common Values

Value Count Frequency (%)
0.5 43
27.2%
0.25 34
21.5%
0.75 33
20.9%
0.0 19
12.0%
1.0 16
10.1%
(Missing) 13
8.2%

Length

2022-07-04T20:37:25.970950 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:26.225962 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.5 43
29.7%
0.25 34
23.4%
0.75 33
22.8%
0.0 19
13.1%
1.0 16
11.0%

Most occurring characters

Value Count Frequency (%)
0 164
32.7%
. 145
28.9%
5 110
21.9%
2 34
6.8%
7 33
6.6%
1 16
3.2%

Most occurring categories

Value Count Frequency (%)
Decimal Number 357
71.1%
Other Punctuation 145
28.9%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 164
45.9%
5 110
30.8%
2 34
9.5%
7 33
9.2%
1 16
4.5%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 502
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 164
32.7%
. 145
28.9%
5 110
21.9%
2 34
6.8%
7 33
6.6%
1 16
3.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 502
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 164
32.7%
. 145
28.9%
5 110
21.9%
2 34
6.8%
7 33
6.6%
1 16
3.2%

u_org
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.5
43
0.75
40
1.0
30
0.25
24
0.0
8

Length

Max length 4
Median length 3
Mean length 3.44137931
Min length 3

Characters and Unicode

Total characters 499
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.25
2nd row 0.25
3rd row 0.0
4th row 0.0
5th row 0.25

Common Values

Value Count Frequency (%)
0.5 43
27.2%
0.75 40
25.3%
1.0 30
19.0%
0.25 24
15.2%
0.0 8
5.1%
(Missing) 13
8.2%

Length

2022-07-04T20:37:26.465114 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:26.718286 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.5 43
29.7%
0.75 40
27.6%
1.0 30
20.7%
0.25 24
16.6%
0.0 8
5.5%

Most occurring characters

Value Count Frequency (%)
0 153
30.7%
. 145
29.1%
5 107
21.4%
7 40
8.0%
1 30
6.0%
2 24
4.8%

Most occurring categories

Value Count Frequency (%)
Decimal Number 354
70.9%
Other Punctuation 145
29.1%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 153
43.2%
5 107
30.2%
7 40
11.3%
1 30
8.5%
2 24
6.8%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 499
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 153
30.7%
. 145
29.1%
5 107
21.4%
7 40
8.0%
1 30
6.0%
2 24
4.8%

Most occurring blocks

Value Count Frequency (%)
ASCII 499
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 153
30.7%
. 145
29.1%
5 107
21.4%
7 40
8.0%
1 30
6.0%
2 24
4.8%

u_balance
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.5%
Missing 14
Missing (%) 8.9%
Memory size 1.4 KiB
0.75
44
0.5
42
1.0
29
0.25
22
0.0
7

Length

Max length 4
Median length 3
Mean length 3.458333333
Min length 3

Characters and Unicode

Total characters 498
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.25
2nd row 0.5
3rd row 0.0
4th row 0.0
5th row 0.75

Common Values

Value Count Frequency (%)
0.75 44
27.8%
0.5 42
26.6%
1.0 29
18.4%
0.25 22
13.9%
0.0 7
4.4%
(Missing) 14
8.9%

Length

2022-07-04T20:37:26.946523 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:27.197695 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.75 44
30.6%
0.5 42
29.2%
1.0 29
20.1%
0.25 22
15.3%
0.0 7
4.9%

Most occurring characters

Value Count Frequency (%)
0 151
30.3%
. 144
28.9%
5 108
21.7%
7 44
8.8%
1 29
5.8%
2 22
4.4%

Most occurring categories

Value Count Frequency (%)
Decimal Number 354
71.1%
Other Punctuation 144
28.9%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 151
42.7%
5 108
30.5%
7 44
12.4%
1 29
8.2%
2 22
6.2%
Other Punctuation
Value Count Frequency (%)
. 144
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 498
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 151
30.3%
. 144
28.9%
5 108
21.7%
7 44
8.8%
1 29
5.8%
2 22
4.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 498
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 151
30.3%
. 144
28.9%
5 108
21.7%
7 44
8.8%
1 29
5.8%
2 22
4.4%

u_assess
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.5
47
0.75
47
0.25
28
1.0
17
0.0
6

Length

Max length 4
Median length 4
Mean length 3.517241379
Min length 3

Characters and Unicode

Total characters 510
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.25
2nd row 0.5
3rd row 0.0
4th row 0.0
5th row 0.5

Common Values

Value Count Frequency (%)
0.5 47
29.7%
0.75 47
29.7%
0.25 28
17.7%
1.0 17
10.8%
0.0 6
3.8%
(Missing) 13
8.2%

Length

2022-07-04T20:37:27.427601 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:27.671023 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.5 47
32.4%
0.75 47
32.4%
0.25 28
19.3%
1.0 17
11.7%
0.0 6
4.1%

Most occurring characters

Value Count Frequency (%)
0 151
29.6%
. 145
28.4%
5 122
23.9%
7 47
9.2%
2 28
5.5%
1 17
3.3%

Most occurring categories

Value Count Frequency (%)
Decimal Number 365
71.6%
Other Punctuation 145
28.4%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 151
41.4%
5 122
33.4%
7 47
12.9%
2 28
7.7%
1 17
4.7%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 510
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 151
29.6%
. 145
28.4%
5 122
23.9%
7 47
9.2%
2 28
5.5%
1 17
3.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 510
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 151
29.6%
. 145
28.4%
5 122
23.9%
7 47
9.2%
2 28
5.5%
1 17
3.3%

u_theory
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.5
66
0.75
27
1.0
26
0.25
19
0.0
7

Length

Max length 4
Median length 3
Mean length 3.317241379
Min length 3

Characters and Unicode

Total characters 481
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.25
2nd row 0.5
3rd row 0.0
4th row 0.0
5th row 0.5

Common Values

Value Count Frequency (%)
0.5 66
41.8%
0.75 27
17.1%
1.0 26
16.5%
0.25 19
12.0%
0.0 7
4.4%
(Missing) 13
8.2%

Length

2022-07-04T20:37:27.890890 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:28.137569 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.5 66
45.5%
0.75 27
18.6%
1.0 26
17.9%
0.25 19
13.1%
0.0 7
4.8%

Most occurring characters

Value Count Frequency (%)
0 152
31.6%
. 145
30.1%
5 112
23.3%
7 27
5.6%
1 26
5.4%
2 19
4.0%

Most occurring categories

Value Count Frequency (%)
Decimal Number 336
69.9%
Other Punctuation 145
30.1%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 152
45.2%
5 112
33.3%
7 27
8.0%
1 26
7.7%
2 19
5.7%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 481
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 152
31.6%
. 145
30.1%
5 112
23.3%
7 27
5.6%
1 26
5.4%
2 19
4.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 481
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 152
31.6%
. 145
30.1%
5 112
23.3%
7 27
5.6%
1 26
5.4%
2 19
4.0%

u_pract
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct 5
Distinct (%) 3.4%
Missing 13
Missing (%) 8.2%
Memory size 1.4 KiB
0.5
44
0.75
38
1.0
37
0.25
22
0.0
4

Length

Max length 4
Median length 3
Mean length 3.413793103
Min length 3

Characters and Unicode

Total characters 495
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.5
2nd row 0.5
3rd row 0.0
4th row 0.0
5th row 0.75

Common Values

Value Count Frequency (%)
0.5 44
27.8%
0.75 38
24.1%
1.0 37
23.4%
0.25 22
13.9%
0.0 4
2.5%
(Missing) 13
8.2%

Length

2022-07-04T20:37:28.366026 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:37:28.621359 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.5 44
30.3%
0.75 38
26.2%
1.0 37
25.5%
0.25 22
15.2%
0.0 4
2.8%

Most occurring characters

Value Count Frequency (%)
0 149
30.1%
. 145
29.3%
5 104
21.0%
7 38
7.7%
1 37
7.5%
2 22
4.4%

Most occurring categories

Value Count Frequency (%)
Decimal Number 350
70.7%
Other Punctuation 145
29.3%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 149
42.6%
5 104
29.7%
7 38
10.9%
1 37
10.6%
2 22
6.3%
Other Punctuation
Value Count Frequency (%)
. 145
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 495
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 149
30.1%
. 145
29.3%
5 104
21.0%
7 38
7.7%
1 37
7.5%
2 22
4.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 149
30.1%
. 145
29.3%
5 104
21.0%
7 38
7.7%
1 37
7.5%
2 22
4.4%

Interactions

2022-07-04T20:36:56.005538 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:06.608222 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:10.115301 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:13.737760 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:17.309085 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:20.862738 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:24.459028 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:27.764279 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:31.300433 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:34.896155 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:38.532264 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:42.178807 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:45.561845 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:48.948731 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:52.445152 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:56.199257 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:06.830177 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:10.321092 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:13.943218 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:17.523501 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:21.077286 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:24.657923 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:27.953037 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:31.517344 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:35.107453 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:38.734105 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:42.376668 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:45.771646 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:49.147188 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:52.840152 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:56.404143 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:07.258805 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:10.533811 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:14.161173 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:17.747141 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:21.290123 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:24.874572 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:28.167468 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:31.744447 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:35.322148 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:38.944169 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:42.591420 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:45.975562 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:49.357022 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:53.053141 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:56.634042 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:07.494873 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:10.763014 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:14.409670 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:17.994438 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:21.535399 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:25.102849 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:28.397077 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:31.988480 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:35.566908 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:39.185297 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:42.829882 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:46.195997 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:49.600232 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:53.288420 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:56.846452 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:07.716321 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:11.005160 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:14.665053 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:18.234575 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:21.757751 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:25.307277 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:28.622523 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:32.224620 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:36.002077 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:39.413949 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:43.049343 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:46.399393 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:49.879565 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:53.512388 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:57.067534 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:07.950985 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:11.242879 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:14.910311 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:18.451669 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:21.999958 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:25.530440 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:28.837442 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:32.465115 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:36.232499 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:39.661776 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:43.290846 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:46.615256 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:50.125190 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:53.749031 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:57.282509 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:08.179615 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:11.475813 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:15.155840 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:18.850041 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:22.227323 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:25.745073 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:29.045930 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:32.700027 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:36.457106 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:39.898256 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:43.514383 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:46.823584 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:50.343662 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:53.968363 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:57.488988 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:08.388598 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:11.701763 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:15.391866 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:19.056576 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:22.448575 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:25.954931 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:29.256263 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:32.919855 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:36.676678 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:40.124746 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:43.737730 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:47.213560 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:50.569778 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:54.180450 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:57.715289 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:08.610693 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:11.946526 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:15.645908 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:19.272296 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:22.681344 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:26.190829 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:29.483953 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:33.153182 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:36.908531 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:40.372887 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:43.969092 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:47.439564 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:50.811057 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:54.407294 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:57.936161 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:08.827078 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:12.194201 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:15.886098 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:19.494806 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:22.906431 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:26.418494 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:29.703914 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:33.389833 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:37.147578 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:40.614377 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:44.196259 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:47.661375 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:51.052129 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:54.642694 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:58.351272 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:09.040937 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:12.421818 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:16.118520 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:19.715007 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:23.129425 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:26.644412 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:30.173496 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:33.650882 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:37.387170 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:40.846798 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:44.421437 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:47.876701 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:51.298812 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:54.870018 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:58.581240 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:09.277402 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:12.654621 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:16.354993 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:19.948368 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:23.354671 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:26.875941 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:30.405050 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:33.957504 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:37.628579 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:41.084927 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:44.655687 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:48.100173 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:51.531108 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:55.098796 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:58.803409 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:09.478068 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:13.075330 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:16.591298 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:20.167044 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:23.573332 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:27.090936 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:30.624594 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:34.187002 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:37.850173 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:41.303923 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:44.868989 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:48.301227 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:51.758789 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:55.315169 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:59.033428 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:09.689727 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:13.307186 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:16.829927 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:20.408360 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:23.803273 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:27.318162 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:30.857030 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:34.417774 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:38.082994 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:41.733393 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:45.095455 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:48.525112 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:51.987830 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:55.549184 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:59.261161 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:09.904281 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:13.528378 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:17.078756 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:20.639796 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:24.032621 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:27.548285 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:31.080377 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:34.661116 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:38.309664 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:41.960990 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:45.339658 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:48.741700 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:52.220663 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:36:55.786426 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-04T20:37:29.101463 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient ( ρ ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r . It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y , one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-04T20:37:29.839180 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient ( r ) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r .

To calculate r for two variables X and Y , one divides the covariance of X and Y by the product of their standard deviations.
2022-07-04T20:37:30.583673 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient ( τ ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y , one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-04T20:37:31.312622 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here .
2022-07-04T20:37:31.945397 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here .

Missing values

2022-07-04T20:36:59.767947 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-04T20:37:02.442147 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-04T20:37:04.051396 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-07-04T20:37:06.917280 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.